Exploration of COVID-19 tracking data from multiple resources
Wei Sun
2020-04-05
1 Introduction
Coronavirus disease 2019 (COVID-19) is an infectious disease caused by a new type of coronavirus: severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The outbreak first started in Wuhan, China in December 2019. The first kown case of COVID-19 in the U.S. was confirmed on January 20, 2020, in a 35-year-old man who teturned to Washington State on January 15 after traveling to Wuhan. Starting around the end of Feburary, evidence emerge for community spread in the US.
We, as all of us, are indebted to the heros who fight COVID-19 across the whole world in different ways. For this data exploration, I am grateful to many data science groups who have collected detailed COVID-19 outbreak data, including the number of tests, confirmed cases, and deaths, across countries/regions, states/provnices (administrative division level 1, or admin1), and counties (admin2). Specifically, I used the data from these three resources:
JHU (https://coronavirus.jhu.edu/)
The Center for Systems Science and Engineering (CSSE) at John Hopkins University.
World-wide counts of coronavirus cases, deaths, and recovered ones.
NY Times (https://www.nytimes.com/interactive/2020/us/coronavirus-us-cases.html)
The New York Times
``cumulative counts of coronavirus cases in the United States, at the state and county level, over time’’
COVID Trackng (https://covidtracking.com/)
COVID Tracking Project
``collects information from 50 US states, the District of Columbia, and 5 other US territories to provide the most comprehensive testing data’’
2 JHU
Assume you have cloned the JHU Github repository on your local machine at ``../COVID-19’’.
2.1 time series data
The time series provide counts (e.g., confirmed cases, deaths) starting from Jan 22nd, 2020 for 253 locations. Currently there is no data of individual US state in these time series data files.
Here is the list of 10 records with the largest number of cases or deaths on the most recent date.
Next, I check for each country/region, what is the number of new cases/deaths? This data is important to understand what is the trend under different situations, e.g., population density, social distance policies etc. Here I checked the top 10 countries/regions with the highest number of deaths.
2.2 daily reports data
The raw data from Hopkins are in the format of daily reports with one file per day. More recent files (since March 22nd) inlcude information from individual states of US or individual counties, as shown in the following figure. So I turn to NY Times data for informatoin of individual states or counties.
3 NY Times
The data from NY Times are saved in two text files, one for state level information and the other one for county level information.
The currente date is
## [1] "2020-04-04"3.1 state level data
First check the 10 states with the largest number of deaths.
## date state fips cases deaths
## 1808 2020-04-04 New York 36 114996 3568
## 1806 2020-04-04 New Jersey 34 34124 847
## 1798 2020-04-04 Michigan 26 14225 540
## 1794 2020-04-04 Louisiana 22 12496 412
## 1779 2020-04-04 California 6 13796 323
## 1826 2020-04-04 Washington 53 7498 318
## 1789 2020-04-04 Illinois 17 10357 248
## 1797 2020-04-04 Massachusetts 25 11736 216
## 1785 2020-04-04 Georgia 13 6383 208
## 1784 2020-04-04 Florida 12 11537 194For these 10 states, I check the number of new cases and the number of new deaths. Part of the reason for such checking is to identify whether there is any similarity on such patterns. For example, could you use the pattern seen from Italy to predict what happen in an individual state, and what are the similarities and differences across states.
Next I check the relation between the cumulative number of cases and deaths for these 10 states, starting on March
3.2 county level data
First check the 10 counties with the largest number of deaths.
## date county state fips cases deaths
## 32269 2020-04-04 New York City New York NA 63307 2254
## 32268 2020-04-04 Nassau New York 36059 13346 396
## 32295 2020-04-04 Unknown New York NA 0 309
## 31904 2020-04-04 Wayne Michigan 26163 6762 252
## 33125 2020-04-04 King Washington 53033 2900 204
## 32299 2020-04-04 Westchester New York 36119 13080 197
## 32194 2020-04-04 Bergen New Jersey 34003 5760 179
## 32290 2020-04-04 Suffolk New York 36103 12328 175
## 31376 2020-04-04 Cook Illinois 17031 7439 167
## 32199 2020-04-04 Essex New Jersey 34013 3584 156For these 10 counties, I check the number of new cases and the number of new deaths.
4 COVID Trackng
The positive rates of testing can be an indicator on how much the COVID-19 has spread. However, they are more noisy data since the negative testing resutls are often not reported and the tests are almost surely taken on a non-representative random sample of the population. The COVID traking project proides a grade per state: ``If you are calculating positive rates, it should only be with states that have an A grade. And be careful going back in time because almost all the states have changed their level of reporting at different times.’’ (https://covidtracking.com/about-tracker/). The data are also availalbe for both counties and states, here I only look at state level data.
Since the daily postive rate can fluctuate a lot, here I only illustrae the cumulative positave rate across time, for four states with grade A data. Of course since this is an R markdown file, you can modify the source code and check for other states.
5 Session information
## R version 3.6.2 (2019-12-12)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Catalina 10.15.4
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] httr_1.4.1 ggpubr_0.2.5 magrittr_1.5 ggplot2_3.2.1
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.3 pillar_1.4.3 compiler_3.6.2 tools_3.6.2
## [5] digest_0.6.23 evaluate_0.14 lifecycle_0.1.0 tibble_2.1.3
## [9] gtable_0.3.0 pkgconfig_2.0.3 rlang_0.4.4 yaml_2.2.1
## [13] xfun_0.12 gridExtra_2.3 withr_2.1.2 dplyr_0.8.4
## [17] stringr_1.4.0 knitr_1.28 grid_3.6.2 tidyselect_1.0.0
## [21] cowplot_1.0.0 glue_1.3.1 R6_2.4.1 rmarkdown_2.1
## [25] purrr_0.3.3 farver_2.0.3 scales_1.1.0 htmltools_0.4.0
## [29] assertthat_0.2.1 colorspace_1.4-1 ggsignif_0.6.0 labeling_0.3
## [33] stringi_1.4.5 lazyeval_0.2.2 munsell_0.5.0 crayon_1.3.4